DarkDeblur: Learning single-shot image deblurring in low-light condition
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866929740398460928 |
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| author | Sharif, S M A Naqvi, Rizwan Ali Alic, Farman Biswas, Mithun |
| author_facet | Sharif, S M A Naqvi, Rizwan Ali Alic, Farman Biswas, Mithun |
| contents | Single-shot image deblurring in a low-light condition is known to be a profoundly challenging image translation task. This study tackles the limitations of the low-light image deblurring with a learning-based approach and proposes a novel deep network named as DarkDeblurNet. The proposed DarkDeblur- Net comprises a dense-attention block and a contextual gating mechanism in a feature pyramid structure to leverage content awareness. The model additionally incorporates a multi-term objective function to perceive a plausible perceptual image quality while performing image deblurring in the low-light settings. The practicability of the proposed model has been verified by fusing it in numerous computer vision applications. Apart from that, this study introduces a benchmark dataset collected with actual hardware to assess the low-light image deblurring methods in a real-world setup. The experimental results illustrate that the proposed method can outperform the state-of-the-art methods in both synthesized and real-world data for single-shot image deblurring, even in challenging lighting environment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_02194 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DarkDeblur: Learning single-shot image deblurring in low-light condition Sharif, S M A Naqvi, Rizwan Ali Alic, Farman Biswas, Mithun Computer Vision and Pattern Recognition Image and Video Processing Single-shot image deblurring in a low-light condition is known to be a profoundly challenging image translation task. This study tackles the limitations of the low-light image deblurring with a learning-based approach and proposes a novel deep network named as DarkDeblurNet. The proposed DarkDeblur- Net comprises a dense-attention block and a contextual gating mechanism in a feature pyramid structure to leverage content awareness. The model additionally incorporates a multi-term objective function to perceive a plausible perceptual image quality while performing image deblurring in the low-light settings. The practicability of the proposed model has been verified by fusing it in numerous computer vision applications. Apart from that, this study introduces a benchmark dataset collected with actual hardware to assess the low-light image deblurring methods in a real-world setup. The experimental results illustrate that the proposed method can outperform the state-of-the-art methods in both synthesized and real-world data for single-shot image deblurring, even in challenging lighting environment. |
| title | DarkDeblur: Learning single-shot image deblurring in low-light condition |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2503.02194 |